187 related articles for article (PubMed ID: 32812412)
1. Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition.
Strbkova L; Carson BB; Vincent T; Vesely P; Chmelik R
J Biomed Opt; 2020 Aug; 25(8):. PubMed ID: 32812412
[TBL] [Abstract][Full Text] [Related]
2. Automated classification of cell morphology by coherence-controlled holographic microscopy.
Strbkova L; Zicha D; Vesely P; Chmelik R
J Biomed Opt; 2017 Aug; 22(8):1-9. PubMed ID: 28836416
[TBL] [Abstract][Full Text] [Related]
3. Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.
Lam V; Nguyen T; Bui V; Chung BM; Chang LC; Nehmetallah G; Raub C
J Biomed Opt; 2020 Feb; 25(2):1-17. PubMed ID: 32072775
[TBL] [Abstract][Full Text] [Related]
4. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.
Nguyen T; Bui V; Lam V; Raub CB; Chang LC; Nehmetallah G
Opt Express; 2017 Jun; 25(13):15043-15057. PubMed ID: 28788938
[TBL] [Abstract][Full Text] [Related]
5. In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning.
Belashov AV; Zhikhoreva AA; Belyaeva TN; Kornilova ES; Salova AV; Semenova IV; Vasyutinskii OS
J Opt Soc Am A Opt Image Sci Vis; 2020 Feb; 37(2):346-352. PubMed ID: 32118916
[TBL] [Abstract][Full Text] [Related]
6. A practical criterion for focusing of unstained cell samples using a digital holographic microscope.
Malik R; Sharma P; Poulose S; Ahlawat S; Khare K
J Microsc; 2020 Aug; 279(2):114-122. PubMed ID: 32441768
[TBL] [Abstract][Full Text] [Related]
7. Automated tracking of temporal displacements of a red blood cell obtained by time-lapse digital holographic microscopy.
Moon I; Yi F; Rappaz B
Appl Opt; 2016 Jan; 55(3):A86-94. PubMed ID: 26835962
[TBL] [Abstract][Full Text] [Related]
8. Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning.
Lam VK; Nguyen TC; Chung BM; Nehmetallah G; Raub CB
Cytometry A; 2018 Mar; 93(3):334-345. PubMed ID: 29283496
[TBL] [Abstract][Full Text] [Related]
9. Movies of cellular and sub-cellular motion by digital holographic microscopy.
Mann CJ; Yu L; Kim MK
Biomed Eng Online; 2006 Mar; 5():21. PubMed ID: 16556319
[TBL] [Abstract][Full Text] [Related]
10. Advantages of Fresnel biprism-based digital holographic microscopy in quantitative phase imaging.
Hayes-Rounds C; Bogue-Jimenez B; Garcia-Sucerquia J; Skalli O; Doblas A
J Biomed Opt; 2020 Aug; 25(8):1-11. PubMed ID: 32755077
[TBL] [Abstract][Full Text] [Related]
11. Plankton classification with high-throughput submersible holographic microscopy and transfer learning.
MacNeil L; Missan S; Luo J; Trappenberg T; LaRoche J
BMC Ecol Evol; 2021 Jun; 21(1):123. PubMed ID: 34134620
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.
Lam VK; Nguyen T; Phan T; Chung BM; Nehmetallah G; Raub CB
Cytometry A; 2019 Jul; 95(7):757-768. PubMed ID: 31008570
[TBL] [Abstract][Full Text] [Related]
13. Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning.
Nissim N; Dudaie M; Barnea I; Shaked NT
Cytometry A; 2021 May; 99(5):511-523. PubMed ID: 32910546
[TBL] [Abstract][Full Text] [Related]
14. Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer-based off-axis digital holographic microscopy.
Min J; Yao B; Trendafilova V; Ketelhut S; Kastl L; Greve B; Kemper B
J Biophotonics; 2019 Sep; 12(9):e201900085. PubMed ID: 31169960
[TBL] [Abstract][Full Text] [Related]
15. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images.
William W; Ware A; Basaza-Ejiri AH; Obungoloch J
Comput Methods Programs Biomed; 2018 Oct; 164():15-22. PubMed ID: 30195423
[TBL] [Abstract][Full Text] [Related]
16. Digital holographic microscopy: a quantitative label-free microscopy technique for phenotypic screening.
Rappaz B; Breton B; Shaffer E; Turcatti G
Comb Chem High Throughput Screen; 2014 Jan; 17(1):80-8. PubMed ID: 24152227
[TBL] [Abstract][Full Text] [Related]
17. Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.
Zikmund T; Kvasnica L; Týč M; Křížová A; Colláková J; Chmelík R
J Microsc; 2014 Nov; 256(2):117-25. PubMed ID: 25142511
[TBL] [Abstract][Full Text] [Related]
18. Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.
Go T; Byeon H; Lee SJ
Biosens Bioelectron; 2018 Apr; 103():12-18. PubMed ID: 29277009
[TBL] [Abstract][Full Text] [Related]
19. TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.
Rubin M; Stein O; Turko NA; Nygate Y; Roitshtain D; Karako L; Barnea I; Giryes R; Shaked NT
Med Image Anal; 2019 Oct; 57():176-185. PubMed ID: 31325721
[TBL] [Abstract][Full Text] [Related]
20. Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.
Kim DNH; Lim AA; Teitell MA
Sci Rep; 2021 Sep; 11(1):19448. PubMed ID: 34593878
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
